Method and apparatus for automated platelet identification within a whole blood sample from microscopy images

ABSTRACT

A method and apparatus for identifying platelets within a whole blood sample. The method includes the steps of: a) adding at least one colorant to the whole blood sample, which colorant is operable to tag platelets; b) disposing the blood sample into a chamber defined by at least one transparent panel; c) imaging at least a portion of the sample quiescently residing within the chamber to create one or more images; and d) identifying one or more platelets within the sample using an analyzer adapted to identify the platelets based on quantitatively determinable features within the image using a analyzer, which quantitatively determinable features include intensity differences.

This application is a continuation of U.S. patent application Ser. No.13/730,095 filed Dec. 28, 2012, which claims priority to U.S.Provisional Patent Application Ser. No. 61/581,887, filed Dec. 30, 2011.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates to methods and apparatus for performinganalyses on whole blood samples from microscopy images in general, andto automated version of the same involving platelets in particular.

2. Background Information

Medical diagnostics often include analyses of a whole blood sample froma patient. One of the more popular diagnostics is a complete blood count(referred to as a “CBC”), which is a suite of tests that includes a“platelet count” (i.e., a thrombocyte count). The platelet count isactually a concentration determination; i.e., number of platelets pervolume. In an adult, a normal platelet count is typically about 150,000to 450,000 platelets per microliter of blood. An abnormal platelet countcan be an indicator of a health problem; e.g., infection, disease, etc.If platelet levels fall below 20,000 per microliter, spontaneousbleeding may occur and is considered a life-threatening risk.

Historically, platelet counts have been performed either by smearing asmall amount of undiluted blood on a slide or by flow cytometry. In thecase of the smear, the sample is applied to a slide and the plateletsand other constituents residing within the smear are counted. Theplatelet count (i.e., platelets per volume within the sample) isestimated based on the relative constituents within the sample.

To perform a platelet count via an electrical impedance or optical flowcytometer, the blood sample must be diluted and then sent through asmall vessel wherein electrical impedance or optical sensors canevaluate constituent cells within the sample as they pass seriallythrough the vessel. The accuracy of these devices can suffer, dependingupon the constituents present within the sample. In an impedancecounter, for example, red blood cell fragments can be construed andcounted as platelets, and giant platelets can be construed and countedas red blood cells (RBCs). In both instances, the accuracy of theautomated platelet count suffers. In addition, the dilution of thesample must be precise or the accuracy is negatively affected, and thediluted sample must be properly disposed of post-analysis. The internalplumbing required to handle the diluted sample often requiresmaintenance and at best contributes considerably to the complexity andcost of the device.

What is needed is an apparatus and method for performing automatedanalyses on a whole blood sample, including a platelet count, which canovercome the limitations of the prior art, including the time requiredto perform the analysis, the operator skill level required to performthe analysis, and one that can provide greater versatility than knownprior art methods and apparatus.

SUMMARY OF THE INVENTION

According to an aspect of the present invention, a method foridentifying platelets within a whole blood sample is provided. Themethod includes the steps of: a) adding at least one colorant to thewhole blood sample, which colorant is operable to tag platelets; b)disposing the blood sample into a chamber defined by at least onetransparent panel; c) imaging at least a portion of the samplequiescently residing within the chamber to create one or more images;and d) identifying one or more platelets within the sample using ananalyzer adapted to identify the platelets based on quantitativelydeterminable features within the image using a analyzer, whichquantitatively determinable features include intensity differences.

According to another aspect of the present invention, an apparatus foridentifying platelets within a whole blood sample is provided. Theapparatus includes an analysis cartridge and an analysis device. Theanalysis cartridge has an analysis chamber with a pair of planarmembers, at least one of which is transparent. At least one colorant isadded to the whole blood sample (e.g., within the cartridge), whichcolorant is operable to tag platelets. The chamber is operable to holdthe sample quiescently between the planar members. The analysis deviceis operable to image at least a portion of the sample quiescentlyresiding within the chamber. The analysis device is adapted to identifythe platelets based on quantitatively determinable features within theimage, which quantitatively determinable features include intensitydifferences.

According to an embodiment of the present invention, intensitydifferences in local regions within the image are determined.

According to an embodiment of the present invention, an image intensityof plasma is accounted for.

According to an embodiment of the present invention, platelet candidatesare evaluated using a directional contrast of an intensity difference.

According to an aspect of the present invention, the image is evaluatedto determine a presence of one or more platelet clusters within thesample.

According to an aspect of the present invention, platelet candidateswithin the image are identified and analyzed using a rule basedclassifier that uses a plurality of quantitative features.

The above described aspects of the present invention and embodiments maybe used individually or in combination with one another, and the presentinvention is not limited to any particular configuration. These andother aspects, embodiments, features, and advantages of the presentinvention will become apparent in light of the detailed description ofthe invention provided below, and as illustrated in the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic view of an analysis device operable to performa platelet analysis according to the present invention.

FIG. 2 is a perspective view of an analysis cartridge having an analysischamber that can be used with the present invention.

FIG. 3 is an exploded, view of the cartridge shown in FIG. 2

FIG. 4 is a top planar view of a tray holding an analysis chamber.

FIG. 5 is a diagrammatic sectional view of an analysis chamber.

FIG. 6 is an image showing platelets and white blood cells within asample under fluorescent imaging.

FIG. 7 is an image showing a glue line/sample interface.

FIG. 8 is an image showing a sample/air interface.

FIG. 9 is an image of a sample entry region of a chamber.

FIG. 10 is a diagrammatic illustration of intensity directional contrastwithin a platelet.

FIGS. 11a-11c are diagrammatic illustrations of Gaussian distributionsof image intensity within a platelet candidate.

FIG. 12 is an image showing a bead within a sample under fluorescentimaging.

FIG. 13 is a diagrammatic view of a platelet candidate within a boundingbox of an orthogonal grid.

FIG. 14 is a diagrammatic view of the platelet candidate shown in FIG.14, now disposed within a convex hull.

FIG. 15 is a flow chart illustrating an embodiment of the present methodfor identifying platelets within a sample via images of the sample.

DETAILED DESCRIPTION OF THE INVENTION

Referring to FIGS. 1 and 2, aspects of the present invention include amethod and an apparatus for identifying and enumerating platelets withina blood sample quiescently residing within an analysis chamber. Theanalysis chamber is typically included within a cartridge 20 that isconfigured for use with an automated analysis device 22, which devicehas imaging hardware and a programmable analyzer adapted to acquire andanalyze images of the sample and thereby identify and enumerateplatelets within the sample.

Referring to FIGS. 2-5, the present invention is not limited to use withany particular analysis chamber embodiment. Examples of acceptableanalysis chambers (and accompanying cartridges) are described in U.S.Pat. No. 7,850,916, and U.S. patent application Ser. Nos. 12/971,860;13/341,618; and 13/594,439, each of which are incorporated herein byreference in its entirety. For purposes of this disclosure, theinvention will be described as using the cartridge and analysis chamberdescribed in U.S. patent application Ser. No. 13/594,439. The analysischamber 24 disclosed in the '439 application includes an upper planarmember 26 and a base planar member 28 attached to a tray 30 that isremovably mounted within the cartridge 20. In some embodiments, aplurality of separator beads 31 (e.g., uniformly sized beads) aredisposed between the upper and base planar members 26, 28, typically incontact with the opposing surfaces of the planar members. FIG. 2 showsthe cartridge 20 in assembled form. FIG. 3 shows an exploded view of thecartridge 20, including the analysis chamber 24 and the tray 30. FIG. 4is a top view of the analysis chamber 24 mounted on the tray 30, andshows an X-Y plane view of the chamber 24. FIG. 5 is a diagrammaticcross-section of the chamber 24, illustrating a Z-X plane view of thechamber 24. The height 32 of the chamber 24 extends along the Z-axis,extending between the opposing interior surfaces 34, 36 of the planarmembers. For whole blood sample analyses, the height 32 of the chamber24 is preferably about four microns (4 μm), but the chamber 24 is notlimited to that height.

Referring to FIG. 1, an analysis device 22 operable to be used with theabove described chamber 24 typically includes an objective lens 34, acartridge positioner 36, one or more sample illuminators 38, one or moreimage dissectors 40, and a programmable analyzer 42. One or both of theobjective lens 34 and cartridge positioner 36 are movable toward andaway from each other to change a relative focal position of the devicerelative to the chamber 24 and the sample disposed therein.

The sample illuminator 38 illuminates the sample using light alongpredetermined wavelengths. For example, the sample illuminator 38 caninclude an epi fluorescence light source and a transmission lightsource. As will be explained below, a colorant such as Acridine Orange(also referred to as “Basic Orange 15” or “ACO”) emit light atparticular wavelengths when mixed with whole blood and subjected to anexcitation wavelength from the epi-fluorescent light source, whichsource typically produces light within the range of about 450-490 nm. Anexcitation wavelength at about 470 nm is particularly useful. Thetransmission light source is operable to produce light at wavelengthsassociated with red and green light, for example. The red light istypically produced in the range of about 600-700 nm, with red light atabout 660 nm preferred. The green light is typically produced in therange of about 515-570 nm, with green light at about 540 nm preferred.Light transmitted through the sample, or fluoresced from the sample, iscaptured using the image dissector 40, and a signal representative ofthe captured light is sent to the programmable analyzer 42, where it isprocessed into an image. The image is produced in a manner that permitsthe light transmittance or fluorescence intensity captured within theimage to be determined on a per unit basis; e.g., “per unit basis” beingan incremental unit of which the image of the sample can be dissected,such as a pixel.

An example of an acceptable image dissector 40 is a charge couple device(CCD) type image sensor that converts light passing through (or from)the sample into an electronic data format image. Complimentary metaloxide semiconductors (“CMOS”) type image sensors are another example ofan image sensor that can be used. The signals from the image dissector40 provide information for each pixel of the image, which informationincludes, or can be derived to include, intensity, wavelength, andoptical density. Intensity values are assigned an arbitrary scale of,for example, 0 units to 4095 units (“IVUs”). Optical density (“OD”) is ameasure of the amount of light absorbed relative to the amount of lighttransmitted through a medium; e.g., the higher the “OD” value, thegreater the amount of light absorbed during transmission. OD can bequantitatively described in optical density units (“ODU”) or fractionsthereof; e.g., a MilliODU is a 1/1000^(th) of an ODU. One “OD” unitdecreases light intensity by 90%. “ODU” or “MilliODU” as a quantitativevalue can be used for images acquired or derived by transmission light.The information from the image dissector 40 may be separated intomultiple channels. For example, the information from the image dissector40 may be separated into three channels. The present invention is notlimited to a three channel embodiment, however. A first of the threechannels may be directed toward information relating to light emittedfrom the sample at a first wavelength (e.g., 540 nm, which appearsgreen). A second channel may be directed toward information relating tolight emitted from the sample at a second wavelength (e.g., 660 nm,which appears red). A third channel may be directed toward informationrelating to light passing through the sample at a third wavelength(e.g., 413 nm, which is used to determine blue optical density—“OD”).The present invention is not limited to these particular wavelengths ornumber of channels.

The programmable analyzer 42 includes a central processing unit (CPU)and is in communication with the cartridge positioner 36, the sampleilluminator 38, and the image dissector 40. The programmable analyzer 42is adapted (e.g., programmed) to send and receive signals from one ormore of the cartridge positioner 36, the sample illuminator 38, and animage dissector 40. For example, the analyzer 42 is adapted to: 1) sendand receive signals from the cartridge positioner 36 to position thecartridge and chamber 24 relative to one or more of the optics,illuminator 38, and image dissector 40; 2) send signals to the sampleilluminator 38 to produce light at defined wavelengths (or alternativelyat multiple wavelengths); and 3) send and receive signals from the imagedissector 40 to capture light for defined periods of time. It should benoted that the functionality of the programmable analyzer 42 may beimplemented using hardware, software, firmware, or a combinationthereof. A person skilled in the art would be able to program theprocessing unit to perform the functionality described herein withoutundue experimentation.

The programmable analyzer 42 is further adapted to process the signalsreceived from the image dissector 40 according to algorithms thatidentify platelets within the sample image, and to distinguish plateletsfrom image characteristics that are similar to platelets, but are notplatelets, and from background characteristics that obscure platelets.

The analysis device 22 is adapted to image a substantially undilutedwhole blood sample disposed within the analysis chamber 24. The sampleis mixed with an amount of a fluorescent dye (or other colorant) that isoperable to stain the platelets contained within the sample. Theaddition and mixing of the dye with the sample could occur at any timeprior to the imaging of the sample; e.g., mixed in the channels of thecartridge prior to passing into the chamber 24 via capillary flow. Thedye permeates through and stains the respective platelets. The dye, uponexcitation, produces fluorescent light emission at particularwavelengths associated with particular colors. The specific color(s) andintensity of the light emitted by the dye within the platelet aretypically a function of a number of factors, including: theconcentration of the dye within the platelet, and the of the platelet.As will be described below, the fluorescent light emission produceslocalized peak emission regions that represent the platelet from whichthey are being emitted.

An example of an acceptable colorant that can be used when performing aplatelet count in a whole blood sample is Acridine Orange (“ACO”). ACOis a fluorescent dye that, when mixed with a whole blood sample, stainsthe platelets and WBCs 50 and reticulocytes) within the sample. Thepresent invention is not limited to using ACO, and other dyes (e.g.,Astrazon Orange) may be used in place of ACO or in combination with ACO.Using ACO as an example, if the sample is subjected to an excitationlight at or about a wavelength of 470 nm, the ACO within the plateletwill emit light at about 540 nm (which appears green) and light at about660 nm (which appears red).

To perform the platelet count, the analyzer 42 is adapted (e.g.,programmed with an algorithm) to direct the sample illuminator 38 toilluminate the sample quiescently residing within the sample withexcitation light (e.g., light at about 470 nm) and transmission light(e.g., light at about 413 nm and at about 660 nm). Upon encountering theexcitation light, the fluorescent dye within each platelet emits greenlight at about 540 nm and red light at about 660 nm. The fluorescentlight emitted from the sample and the transmission light passing throughthe sample is captured using the image dissector 40, and a signalrepresentative of the captured light is sent to the programmableanalyzer 42, where it is processed into an image. The image is producedin a manner that permits the fluorescence and transmission intensitycaptured within the image to be determined on a per unit basis.

The programmable analyzer 42 is adapted to collect the image datasignals from the image dissector 40 and process those image data signalsto facilitate the identification of platelets shown within the image.The programmable analyzer 42 is also adapted to determine the volume ofthe sample quiescently residing within the analysis chamber 24. Forexample, the algorithm is adapted to identify perimeters of the samplewithin the chamber 24 such as glue line 44 sample 46 interfaces (e.g.,see FIG. 7), which glue lines 44 form lateral boundaries of the chamber24, and sample 46/air 48 interfaces 49 (e.g., see FIG. 8) that existtypically at the edges of the sample 46 that do not encounter a glueline 44. The height 32 of the chamber 24 is known or determinable. Oncethe area of the chamber 24 occupied by the sample 46 is determined(e.g., each pixel of the image has an associated chamber area), thevolume of the sample can be determined using the sample area and theheight associated with the chamber 24.

In some embodiments the image data signals are initially processed witha smoothing algorithm that filters the signals to make the backgroundportions of the image more uniform. An example of an acceptablesmoothing algorithm is one that applies a morphology filter (e.g., animage opening filter) to the image data. The filter is operable toalleviate some background variations and can be used to remove largebright objects from the image such as white blood cells 50 (“WBCs”).WBCs 50 can appear as intensity peaks due to material contained withinthe WBCs 50 (e.g., RNA, DNA) that is highlighted by the colorant used tohighlight the platelets. FIG. 6 illustrates platelets 52 and WBCs 50within an image. WBCs 50 can be distinguished from platelets 52 however,based on their large light intensity relative to platelets 52.Eliminating the WBCs 50 from the image (e.g., by a segmentation process)facilitates the identification of the platelets 52.

The image data is also analyzed to identify local intensity peaks. Thislocal peak identification process can be performed before or after the“smoothing” process, but performing the smoothing step first eliminatessome potential sources of error prior to the local peak intensitydetermination. This smoothing step is not required, however. Theidentification of the local intensity peaks at one or more definedwavelengths can be performed using a variety of different techniques. Aswill be described below, image intensity can vary substantiallythroughout the sample, which variations can be attributable to factorssuch as plasma intensity variation, glue line proximity, WBCconcentration, RBC concentration, etc. The accuracy of the plateletidentification is enhanced by quantitatively evaluating intensitydifferences (i.e., peaks) on a local basis. The term “local” as usedherein refers to defined small areas within the sample quiescentlyresiding within the chamber 24, which areas can be defined in terms of apredetermined pixel region; e.g., a 5×5 square of pixels. A 5×5 pixelsquare is useful when evaluating platelets 52 because a typical platelet31 of about 2-3 μm size fits within the 5×5 pixel square at theresolution used for the imaging. The present invention is not limited to“local regions” of this particular size, however. In each of these localregions, a maximum sample image intensity value is determined. Forexample, the sample can be subjected to a fluorescent excitation light,and a sample image acquired, which image includes emitted lightintensity; e.g., emitted light intensities within the green fluorescentchannel. Once the image intensity peaks are determined in the respectivelocal regions, a global threshold can be applied to eliminate thoseintensity peaks (e.g., maximums) below the global threshold. Thisidentification process establishes all of the portions within the imagethat can potentially represent a platelet 31, which portions are eachreferred to hereinafter as a “platelet candidate”. Once all of thepotential platelet candidates are identified, then the image data isfurther analyzed to eliminate those candidates that are not platelets52, and to identify platelet clumps that may be present within theimage.

The image intensity of the sample image portions that are contiguouswith the analysis chamber glue lines 44 and the sample/air interface(s)49 can be contaminated by light intensity effects caused by the gluelines 44 and the sample/air interface(s) 49. For example, as can be seenin FIG. 7, when the sample is illuminated the glue lines 44 appearbright, having high image intensity. The high image intensity of theglue lines 44 causes the contiguous areas to have greater intensity thanthey would otherwise, thereby increasing the possibility of mistakenplatelet identification, or the possibility that platelets 52 will bemissed because of the overall intensity. The same effect occurs to somedegree at the sample/air interface 49 as can be seen in FIG. 8. Atechnique that can be algorithmically implemented by the analyzer 42 toaccount for the intensity contamination in the contiguous image portions(i.e., anomaly areas) is to remove those contiguous image portions fromconsideration during the platelet count; e.g., by masking, etc. Thedetermination of how much sample image is removed may be made, forexample, by evaluating relevant historical data. For example, removal ofabout 100 pixel lines of the sample image contiguous with a glue line istypically adequate to eliminate intensity contamination attributable tothat glue line 44. Similarly, removal of about 80 pixel lines of thesample image contiguous with a sample/air interface 49 is typicallyadequate to eliminate intensity contamination attributable to thatinterface. To account for platelets 52 present in the removed sampleimage portions, the number of platelets in that area can be estimatedbased on relative numbers of platelets determined in regions of thesample image local to the removed area.

In some embodiments, the algorithm utilized within the processor 42 canbe further adapted to recognize other areas where platelet recognitionis problematic. For example, in some instances a region of an analysischamber 24 may have discrepancies that will inhibit an accurate volumedetermination. In such an instance, the ability to do an accurateplatelet count (which is a function of volume) in that area may becompromised. FIG. 9, for example, shows a chamber entry region that hasimage anomalies 58 due to excessive sample in the area. To account forsuch anomalies, the number of platelets 52 in the area can be estimatedbased on platelet counts in areas of the sample local to the problematicarea.

Another technique for facilitating the identification of the platelets52 that can be algorithmically implemented by the analyzer 42 is aremoval of background existing within the initially acquired image. Forexample, a filter can be applied to the image data signals that removesvariations in intensity (e.g., green light intensity) below apredetermined global threshold. Local intensity maximums below theglobal threshold that might otherwise be identified as platelets 52 canbe eliminated, thereby eliminating the possibility that those intensitypeaks are incorrectly identified as platelets 52. Segmentationtechniques, for example, can be used as a mechanism for removing thebackground. The present invention is not limited to any particularsegmentation technique, and a specific technique can be chosen in viewof the application at hand. The present invention is also not limited tousing a segmentation technique to remove background, and can use othertechniques that select (i.e., “pick”) pixels or otherwise distinguishpixels having particular attributes.

Another technique for facilitating the identification of the platelets52 that can be algorithmically implemented by the analyzer 42 involvesaccounting for (e.g., estimating) the image intensity of plasma withinlocal areas of the sample. Plasma typically appears brighter (i.e.,higher image intensity) than RBCs but not as bright as platelets 52. Atleast some of the fluorescent dye added to the sample can reside withinthe plasma, and as a result illuminating the sample with excitationlight creates some level of emitted light intensity within the plasma.The distribution of dye within the plasma may not, however, be uniformwithin the entire sample. Consequently, the image intensity of plasmacan vary significantly within the sample disposed within the analysischamber 24. For example, imaging data indicates that the image intensityof plasma within a first area of the sample can vary as much as 30-40%from the image intensity of plasma in a second area of the sample. Thelack of uniformity within the plasma intensity makes it difficult touniformly account for plasma intensity without negatively affecting theplatelet identification process. The non-uniformity of plasma intensityis particularly problematic in regions within the sample where largenumbers of RBCs 54 reside. If the plasma intensity is not accounted for,small areas of plasma visible within RBC regions can appear as localintensity peaks which could then be mistakenly identified as platelets52. To address this issue, the algorithm can be adapted to apply a RBCmask in the RBC regions to segment out the RBCs 54 (or otherwise removethem from the image), leaving the remaining plasma areas. For example,RBCs 54 have no expression in the red channel of image signal data. Animage formed using a red channel mask, therefore, will only show theintensity of the plasma and whatever platelets 52 are within that RBCregion. The platelets 52 can then be quantitatively distinguished fromthe local plasma by virtue of the difference in intensity (e.g., thosepixels that are about 20% greater than the surrounding pixels) betweenthe two sample constituents. In those RBC regions that do not havesufficient plasma areas to permit this type of comparative analysis, analternative technique can be used based on the image intensity of theRBCs 54 themselves. For example, the estimated plasma image intensity inthose regions can be based on the following estimation: (RBC averageimage intensity)+3*(RBC image intensity standard deviation)=estimatedplasma intensity. The present method is not limited to this particularalternative technique.

After one or more of the above described techniques are applied to theplatelet candidates and non-platelet candidates (to the extent they areidentified) are removed from consideration, the remaining candidates canbe further analyzed by evaluating the characteristics of each individualcandidate. A first technique that can be algorithmically implemented bythe analyzer 42 involves evaluating the directional contrast of thecandidate's intensity peak within a given area. The area can be definedin terms of pixels surrounding the intensity peak. For example, using aresolution that is useful for a whole blood analysis (e.g., 0.5μm/pixel), the area potentially representing the platelet candidate canbe defined as up to about three or four (3 or 4) pixels outwardly fromthe intensity peak, and the area outside of the four (4) pixels definedas being outside the platelet candidate. The present invention is notlimited to these area definitions, which can be selected to suit theapplication at hand. Platelets 52 have a directional intensity contrastwithin the defined area, wherein the intensity decreases outwardly fromthe center of the area (i.e., outwardly from the intensity peak,diagrammatically shown in FIG. 10) in a direction toward the perimeterof the area. For example, the intensity of the peak is at a maximum inthe center and decreases along a slope extending outwardly toward thearea perimeter. In terms of the image, which is step-wise segmented bythe pixels forming the image, each pixel has a decrease in intensitytraveling in the direction toward the area perimeter. This incrementaldecrease in intensity exists in a plurality of directions out from themaximum intensity value at the center of the area, but not necessarilyin all directions. The circumferential uniformity of the directionalintensity contrast can be evaluated by quantitatively evaluating theimage intensity of pixels every “Y” degrees of rotation (e.g., every 30degrees) around the intensity peak of the candidate. The rate of theintensity decrease per pixel can also vary to suit the application athand. In addition, a first intensity decrease percentage can be used ina first region of the sample, and a second higher percentage in otherregions of the sample where platelet identification is more difficult;e.g., use a higher intensity decrease percentage in regions of higherplasma intensity, or in RBC regions. FIG. 10 diagrammatically depicts anintensity peak of a platelet candidate, illustrating an incrementaldecrease in intensity from the center of the area outwardly in a singledirection toward the perimeter of the area.

Another technique for algorithmically evaluating the characteristics ofan individual candidate involves quantitatively determining a Gaussiandistribution of the incremental decrease in intensity surrounding thecandidate's intensity peak in a given area (which area is defined aboverelative to directional contrast). FIGS. 11a-11c diagrammaticallyillustrate three different Gaussian distributions of intensitysurrounding an intensity peak. In FIG. 11a , a sharply defined intensitypeak is located in the center of the area, and the intensitydistribution decreases uniformly traveling away from the center peak;e.g., pixel to pixel decrease in intensity of about 4%. This intensitydistribution is very typical of platelet images and platelet candidateshaving this distribution are accepted as platelet images. In FIG. 11b ,the distribution illustrates an intensity peak within the area center,surrounded by a region of nearly the same intensity (e.g., less than 3%variation in intensity amongst the central pixels), which region in turnis surrounded by a region of relatively large intensity decrease in theoutward direction. This intensity distribution (which appears as havinga large intensity peak area) is less typical of platelet images and maybe a function of the image being over exposed. Depending upon thecircumstances of the image, platelet candidates having this type ofGaussian distribution may be accepted as a platelet; e.g., accepted whenthey favorably compare to local plasma intensity values, etc. In FIG.11c , the distribution illustrates a relatively sharp intensity peakwithin the center of the area, surrounded by a sharp decrease inintensity in the outward direction. This intensity distribution is notindicative of a platelet, and these platelet candidates are not acceptedas platelets 52. One or both of the Gaussian distribution anddirectional contrast analyses can be performed on a platelet candidate.

Within a sample of substantially undiluted whole blood, platelets 52 canaggregate into clusters that show up in the image as a mass havingmultiple intensity peaks. If a cluster is considered to be only a singleplatelet, the number of platelets 52 identified will be less than areactually present in the cluster. To avoid this type of error, theanalyzer 42 can be algorithmically adapted to identify platelet clustersand distinguish them from single platelets 52. One method foridentifying and distinguishing the clusters involves the above-describedGaussian distribution analysis. This technique utilizes the outer regionof the area defined as a platelet candidate within the Gaussiandistribution analysis (e.g., the area within a radius of “x” pixels). Toidentify a cluster, the image intensity of the pixels at the perimeterare compared to the local plasma intensity. If a number of the candidateperimeter pixels (e.g., 50%) each have an image intensity that is apredetermined percentage greater than the local plasma image intensity,then the candidate is deemed to be a cluster. This is an example of amethod for identifying a cluster and the present invention is notlimited to this particular example.

Once a cluster is identified, the number of platelets 52 within thecluster can be determined using a variety of techniques. For example,the algorithm can be adapted to determine a threshold image intensityvalue (T_(h)) for each peak in the cluster. The threshold imageintensity value (T_(h)) is determined based on the image intensity valueof that particular peak, and a local plasma intensity value. Thethreshold value (T_(h)) is less than the respective peak intensityvalue, but is greater than the local background intensity values. Todetermine the number of platelets 52 within a given cluster, a “grow”technique is applied by the algorithm to the identified cluster. Underthe grow technique, the image units (e.g., pixels) that are contiguouswith the peak intensity image units, and which have an intensity valueequal to or greater than the threshold intensity value (T_(h)), areidentified as part of the cluster. The process then applies the samethreshold evaluation to the pixels contiguous with the pixels lastidentified as part of the cluster. The process is repeated until noadditional contiguous pixels at an intensity level greater than thethreshold level (T_(h)) are found.

Once the cluster is “grown”, the area associated with each grownplatelet (i.e., each body expanded outwardly from an image intensitypeak as described above) within the cluster is determined and an average(PLT_(avg grown area)) area value of those expanded regions isdetermined. That average area value (PLT_(avg grown area)) is thencompared to a known average normal platelet area(PLT_(avg normal area)). If the average grown area(PLT_(avg grown area)) is greater than a multiplier times the averagehuman platelet area (e.g., α*PLT_(avg normal area), where α may equal1.x) then the number of platelets 52 within the cluster is defined bythe total area of the cluster (A_(cluster)) divided by the average humanplatelet area (e.g., A_(cluster)/PLT_(avg normal area)). If the averagegrown area (PLT_(avg grown area)) is less than the aforesaid multipliertimes the average human platelet area, then the number of platelets 52within the cluster is defined by the total area of the cluster dividedby the average grown platelet area (e.g.,A_(cluster)/PLT_(avg grown area)).

For those platelet analyses that utilize an analysis chamber 24 havingbeads 31 disposed within the chamber 24 (e.g., a chamber such as thatdisclosed in the '114 application), the beads 31 may appear in the imageas having a bright ring around their perimeter; i.e., a ring of highintensity within the image. The image shown in FIG. 12 includes aplurality of beads 31, each with a ring of high image intensity aroundits perimeter bead 31.

To avoid possible platelet identification error associated with thebeads 31 (e.g., within the bright ring, or in close proximity ring, orinside of the ring), the analyzer 42 can be adapted to evaluate theintensity peaks associated with a bead 31 using a rule based classifierbased on a plurality of features. The present invention is not limited,however, to using the classifier solely for the purpose of analyzingbead contiguous regions for platelets 52.

As an initial step, beads 31 are identified within the image sample. Forexample, beads 31 can be identified within the sample image created todetermine the sample volume, in which image they appear as a dark spot;e.g., in some instances the identified beads may be masked to moreclearly appear as a dark spot to facilitate the analysis. The inventionis not limited to this technique for identifying beads 31, however.

Once a bead 31 is identified, each image intensity peak within the beadarea is identified and is considered to be a platelet candidate. Tofurther evaluate a platelet candidate, that candidate may then besubjected to a “grow” technique as is described above to determinewhether the candidate is a platelet candidate or possibly a plateletcluster.

For each intensity peak (e.g., located proximate a bead 31) that isidentified as a platelet candidate, that candidate may then be furtherevaluated using the rule based classifier. Within the steps of the rulebased classifier, each platelet candidate is analyzed using at leastsome of a plurality of quantitative features including: Area, NormalizedPeak Intensity Value, Normalized Average Intensity Value, NormalizedEstimated Plasma Intensity, Roundness, Extent, Solidity, Eccentricity,Major Axis Length, and Minor Axis Length. These quantitative featuresare examples of characteristics that can be used within the rule basedclassifier, but the present invention is not limited to these specificfeatures. In addition, the present invention is not limited to using anyparticular number or combination of these features; e.g., in someinstances the rule based classifier may use as few as one feature or asmany as all the features during the classifying process.

For example, to evaluate a particular platelet candidate, the classifiermay first consider the candidate Area. A quantitative value for thecandidate Area can be assigned based on the number of pixels that thecandidate platelet occupies within the image.

The rule based classifier is also adapted to consider a quantitativevalue representative of a Normalized Peak Intensity Value of theplatelet candidate. For example, as indicated above, image intensityvalues may be assigned an arbitrary scale of, for example, 0 units to4095 units (“IVUs”). In this feature, the peak image intensity value maybe normalized by dividing the determined value by 4095. As indicatedabove, the intensity scale of 0-4095 is an arbitrary scale and thepresent invention is not limited to this particular scale.

The rule based classifier is also adapted to consider a quantitativevalue representative of a Normalized Average Intensity Value of theplatelet candidate. In this feature, the image intensity values of thepixels within the platelet candidate are averaged and normalized bydividing the determined value by 4095. Here again, the intensity scaleof 0-4095 is an arbitrary scale and the present invention is not limitedto this particular scale.

The rule based classifier is also adapted to consider a quantitativevalue representative of a Normalized Estimated Plasma Intensity ofplasma local to the platelet candidate. In this feature, in a manner thesame as or similar to that described above, local plasma regions areidentified and a representative image intensity value of those plasmaregions is determined; e.g., an average intensity value for the localplasma regions is determined. The representative image intensity valuefor the local plasma regions is then normalized in a manner such as thatdescribed above; e.g., the value divided by 4095.

The rule based classifier is also adapted to consider a quantitativevalue representative of the Roundness of a platelet candidate. Onetechnique for determining the Roundness of a platelet candidate involvesutilizing the following equation:

${Roundness} = \frac{{Perimeter}^{2}}{4\;{\pi \cdot {Area}}}$where the term Perimeter is defined as the distance around the perimeterof the platelet candidate, and the term Area is the area of the plateletcandidate.

The rule based classifier is also adapted to consider a quantitativevalue representative of the Extent of a platelet candidate. In thisfeature, a quantitative value representative of the proportion of thepixels in a bounding box 60 that are also within the “segmented mask” 62is determined, which value is referred to as the Extent. The “segmentedmask” 62 refers to the area occupied by the platelet candidate withinthe bounding box 60. An example of a bounding box 60 is the smallest boxin an orthogonal grid 64 that encloses the segmented mask 62. FIG. 13diagrammatically illustrates a segmented mask 62 of an elliptical-shapedplatelet candidate disposed within a bounding box 60 portion of anorthogonal grid 64.

The rule based classifier is also adapted to consider a quantitativevalue representative of the Solidity of a platelet candidate. In thisfeature, a quantitative value representative of the proportion of pixelsin the convex hull 66 that are also in the segmented mask 62 isdetermined, which value is referred to as the Solidity. As indicatedabove, the segmented mask 62 refers to the area occupied by the plateletcandidate. The convex hull 66 can be defined as, for example, thesmallest box into which the segmented mask 62 can fit, which hull 66 maynot be orthogonally aligned. FIG. 14 diagrammatically illustrates theelliptical-shaped segmented mask 62 shown in FIG. 13, now disposedwithin a convex hull 66.

The rule based classifier is also adapted to consider a quantitativevalue representative of the Eccentricity of a platelet candidate. Inthis feature, a quantitative value representative of a ratio of adistance between the foci of the ellipse and its major axis length isdetermined.

The rule based classifier is also adapted to consider a quantitativevalue representative of the Major Axis Length of a platelet candidate.In this feature, a quantitative value representative of the length (inpixels) of the major axis of the ellipse that has the same normalizedsecond central moments as the segmented mask is determined.

The rule based classifier is also adapted to consider a quantitativevalue representative of the Minor Axis Length of a platelet candidate.In this feature, a quantitative value representative of the length (inpixels) of the minor axis of the ellipse that has the same normalizedsecond central moments as the segmented mask is determined.

The quantitative value of each feature may vary, to some degree, withina sample population from a particular subject, and may also vary betweensubjects. The present invention addresses this variability by typicallyutilizing a plurality of features to evaluate a platelet candidate. Byusing more than one feature to evaluate and identify a plateletcandidate, the present method decreases the potential for any particularfeature to have an adverse effect on the accuracy of the evaluation. Thevariability can also be addressed by selectively adjusting the magnitudeof the quantitative reference value(s) associated with each feature.

In some embodiments, the rule based classifier is a learned model basedclassifier. Training sample images are used to train the classifier, andthe trained classifier in turn builds the learned model. Once thelearned model is developed, that model is then utilized to evaluatefeatures (e.g., such as those described above) associated with aplatelet candidate image from a sample, and to include or exclude theplatelet candidate based on those features.

The training sample images can be empirically collected platelet images;e.g., platelet images collected by a skilled technician. In someembodiments, the training sample images may be organized in setsassociated with each feature. The number of platelet images is selectedto provide sufficient data for each feature for training purposes; i.e.,sufficient data to enable the classifier to be trained with anacceptable level of accuracy for the feature analysis. The learned modelused within this embodiment is not limited to any particular sizetraining set. Often a training set can contain hundreds to thousands ofeach type of platelet feature to faithfully represent the variabilitywithin different people, different imaging conditions and etc.

The classifier can be trained (and the learned model developed) byevaluating each platelet image within a training set to providequantitative reference value(s) for each feature for each platelet. Thecollective reference values (or statistical representations thereof) canthen be used to build the learned model. The learned models permit thepresent application to adjust based on actual image data, and theautomated interpretation of that image data, and thereby provide adesirable level of accuracy. The present invention is not limited to anyparticular type of learned model. Examples of acceptable types oflearned models include a neural network model such as a MultilayerPerceptron, or a statistical model such as a Bayesian classifier, or alinear model such as a Support Vector Machine (SVM). All these types oflearned models are well known and the present invention is not limitedto any particular embodiment thereof. In some embodiments, combinationsof these models (and/or others) can be used.

In the operation of the invention, an undiluted sample of whole blood iscollected into a disposable cartridge such as that illustrated in FIGS.2-5. Reagents, including one or more colorants (e.g., ACO) and ananticoagulant (e.g., EDTA), are added to the sample to facilitate theplatelet analysis. The sample admixed with the reagents is depositedwithin the analysis chamber portion of the cartridge, where itquiescently resides during the imaging process. The cartridge isinserted into (or otherwise engaged with) the analysis device 22, whereit is appropriately positioned by the cartridge positioner 36 relativeto the objective lens, sample illuminator 38, and image dissector 40,and is subsequently imaged.

In most instances, the analysis device 22 is programmed to image theentirety of the sample quiescently residing within the chamber 24. Insome applications, however, a portion of the sample can be imaged. Theimaging process can vary depending upon the application at hand. For theplatelet analysis described above, the imaging process involvessubjecting the sample to a fluorescent excitation light source; e.g.,light at about 470 nm from the epi-fluorescent light source. Theexcitation light source causes the colorant combined with elementsdisposed within the sample to emit fluorescent light at two differentwavelengths (e.g., red˜660 nm, and green˜540 nm). The image dissector 40captures the light fluorescing from the sample and provides signalsrepresentative of the intensity and color (i.e., wavelength) of thecaptured light. The signals are processed into a form that permits theprogrammable analyzer 42 to form an image of the sample based on thesignals, which image can be quantitatively analyzed to perform theplatelet analysis.

The flow chart shown in FIG. 15 provides an example of the processthrough which the platelets 52 in a sample, imaged as described above,can then be identified and enumerated according to the presentinvention. The present invention does not require all of the stepsidentified in FIG. 15 be performed in all embodiments, and also is notlimited to the particular order of steps shown in FIG. 15.

The first step shown in FIG. 15 involves applying a blood volume mask tothe image to determine the area occupied by the sample within the image,which can then be used to determine the volume of the sample within theanalysis chamber 24. A RBC mask can also be generated for later use indistinguishing platelets 52 in certain regions.

The second step involves removal of background from the sample image. Asindicated above, background portions of the images can be identified byvarious techniques including thresholding, and once identified can beremoved by filter, segmentation, etc.

The third step involves identifying image intensity peaks by identifyinglocal intensity maximums in a given area of image.

The fourth step involves identifying WBCs 50 within the sample image, orimage portions, and excluding those regions as image intensity regionsthat are not platelet candidates.

The fifth step involves identifying beads 31 within the sample image andpreparing a mask that can be applied to the image portions representingbeads 31.

The sixth step involves estimating plasma intensity in local regionswhere required to identify platelets 52; e.g., local plasma intensityvalues can be used to distinguish local intensity peaks attributable toplatelets 52 from local intensity peaks attributable to plasma. Thisstep may be performed selectively in particular regions within thesample; e.g., regions containing large numbers of RBCs 54.

The seventh step involves analyzing particular platelet candidates todetermine whether the image intensity peak of the candidate hasdirectional contrast in one or more directions extending outwardly fromthe peak.

The eighth step involves analyzing the Gaussian distribution of theimage intensity of particular platelet candidates. The nature of thedistribution, as described above, provides information relating to theprobability of a particular candidate being an actual platelet.

The ninth step involves removal of elements within the sample image thatare attributable to imperfections present in the imaging system; e.g.,scratches in the analysis chamber panels, debris on the panels, etc.

The tenth step involves identifying platelets 52 from candidates usingan image growing technique. This technique can be applied to a varietyof different types of platelet candidates. For example, the growingtechnique can be applied to a platelet candidate that is identified as apotential platelet clump. The growing technique, an example of which isdescribed above, provides a means to evaluate the area occupied by theplatelet candidate (or clump). The area provides information that can beused subsequently to evaluate whether the candidate is actually aplatelet. If the candidate is a clump, the area provides informationthat can be used to determine the number of platelets 52 residing withinthe clump.

The eleventh step involves estimating the number of platelets 52 withinthose candidates determined to be clumps. Examples of mathematicaltechniques that can be used to estimate the number of platelets 52within a clump are described above. The present invention is not limitedto these particular algorithms.

The twelfth step involves classifying platelet candidates that arelocated in close proximity to separator beads 31 disposed within thechamber 24. As indicated above, in some embodiments of the presentinvention the programmable analyzer 42 is adapted with an algorithm thatincludes a rule based classifier that may include a learned model. Theplatelet candidates are evaluated using the classifier and theidentified platelets 52 are enumerated.

Once the actual platelets 52 are identified, the platelets 52 within thedetermined sample volume can be reported in a platelet number per volumevalue, or other useful form, as indicate in the thirteenth step.

Although this invention has been shown and described with respect to thedetailed embodiments thereof, it will be understood by those skilled inthe art that various changes in form and detail thereof may be madewithout departing from the spirit and the scope of the invention.

What is claimed is:
 1. A method for identifying platelets within abiologic fluid sample, comprising: adding at least one colorant to thesample, which colorant is operable to tag platelets; disposing thesample into a chamber defined by at least one transparent panel; imagingat least a portion of the sample quiescently residing within the chamberto create one or more images; and identifying one or more plateletswithin the sample using an analyzer adapted to identify the plateletswithin the image, which identifying includes comparing an imageintensity of plasma to an image intensity of the one or more platelets.2. The method of claim 1, wherein the identifying includes comparing theimage intensity of plasma to the image intensity of the one or moreplatelets from one or more local regions within the image.
 3. The methodof claim 2, wherein the image intensity of the plasma is an intensity offluorescent light emitted from the plasma and the image intensity of theone or more platelets is an intensity of fluorescent light emitted fromthe one or more platelets.
 4. The method of claim 1, further comprisingidentifying anomaly image portions and estimating the number ofplatelets in the anomaly image portions.
 5. The method of claim 1,wherein the identifying includes evaluating platelet candidates using adirectional contrast of an intensity difference.
 6. The method of claim5, further comprising determining a peak intensity difference anddetermining a value of an incremental decrease in intensity within theimage in a direction extending away from the peak.
 7. The method ofclaim 6, further comprising determining a circumferential uniformity ofthe incremental decrease in intensity.
 8. The method of claim 1, whereinthe identifying includes evaluating the image to determine a presence ofone or more platelet clusters within the sample.
 9. The method of claim1, which said identifying is performed by a central processing unit ofan analysis device.
 10. An apparatus for identifying platelets within abiologic fluid sample, comprising: an analysis cartridge having ananalysis chamber defined by a pair of members, which cartridge isoperable to add at least one colorant to the sample, which colorant isoperable to tag platelets, and which chamber is operable to hold thesample quiescently between the members; and an analysis device that isoperable to image at least a portion of the sample quiescently residingwithin the chamber, and which analysis device is adapted to identify theplatelets within the image by comparing an intensity of the plasma to animage intensity of one or more platelets within the sample.
 11. Theapparatus of claim 10, wherein the analysis device is adapted to comparethe image intensity of the plasma to the image intensity of one or moreplatelets within local regions in the image.
 12. The apparatus of claim11, wherein the image intensity of the plasma is an intensity offluorescent light emitted from the plasma and the image intensity of theone or more platelets is an intensity of fluorescent light emitted fromthe one or more platelets.
 13. The apparatus of claim 10, wherein theanalysis device is adapted to identify anomaly image portions andestimate the number of platelets in the anomaly image portions.
 14. Theapparatus of claim 10, wherein the analysis device is adapted toevaluate platelet candidates using a directional contrast of anintensity difference.
 15. The apparatus of claim 14, wherein theanalysis device is adapted to determine a peak intensity difference andto determine a value of an incremental decrease in intensity within theimage in a direction extending away from the peak.
 16. The apparatus ofclaim 15, wherein the analysis device is adapted to determine acircumferential uniformity of the incremental decrease in intensity. 17.The apparatus of claim 10, wherein the analysis device is adapted toevaluate the image to determine a presence of one or more plateletclusters within the sample.